ForkMind 🧠

Local-first LLM state branching & debugging. ForkMind treats AI context windows like a Git repository: it captures every LLM call into a local .forkmind/ directory, visualizes the conversation as a Directed Acyclic Graph (DAG), and lets you branch alternative prompts or model params from any point in the history — all on your machine, no cloud, no account.

Works with any OpenAI-compatible API, defaulting to free, open-source models via Ollama. Also supports Anthropic and any hosted free tier (Groq, OpenRouter, Together, vLLM, LM Studio).

Live demo: a conversation tree with a branch off the root, the node inspector (request/response, tokens, provenance), and the Fork from here dialog.

Why

Debugging agentic / tool-calling flows means re-running the same prompt with tiny tweaks over and over. ForkMind records each run as a node, so you can:

• See the whole conversation tree, including tool calls and token usage.

• Branch from any historical turn — edit the prompt, swap the model, re-run.

• Compare outcomes visually instead of scrolling through terminal logs.

Everything is plain JSON on disk. No database. No telemetry.

Related MCP server: dakera-mcp

Install

# Run without installing (published on npm)
npx forkmind init
npx forkmind start

# …or install the CLI globally
npm install -g forkmind
forkmind start

No npm registry needed either — ForkMind runs straight from the git link, and the dashboard builds automatically on install:

# Run without installing, from GitHub
npx github:medhovarsh/forkmind init
npx github:medhovarsh/forkmind start

# …or clone to hack on it
git clone https://github.com/medhovarsh/forkmind
cd forkmind && npm install

Install as a Claude Code plugin

ForkMind ships a Claude Code plugin (skill + /forkmind command) so Claude knows when and how to drive it — same install flow as any marketplace plugin:

/plugin marketplace add Medhovarsh/forkmind
/plugin install forkmind

The plugin bundles:

• forkmind skill — Claude reaches for ForkMind whenever you ask it to debug a prompt, compare models, branch from a past turn, or regression-test a call.

• /forkmind command — start / branch / test / mcp on demand.

• forkmind-debugger agent — runs model/prompt comparisons in an isolated context and returns a compact verdict instead of dumping transcripts.

• MCP server, auto-wired — agents query their own .forkmind/ history (recall attempts, trace lineage, self-correct) with zero manual config.

The CLI is still what runs the proxy + dashboard; the plugin is the glue that teaches Claude to use it.

Quick start (free, no API key)

# 1. Install a free local model
#    (install Ollama from https://ollama.com first)
ollama pull llama3

# 2. Init + start ForkMind
npx github:medhovarsh/forkmind init    # create .forkmind/ in your project
npx github:medhovarsh/forkmind start   # proxy on http://localhost:4500 + dashboard

# 3. Point your code at the proxy (see SDK below), make some calls

# 4. Open the dashboard
open http://localhost:4500

Drop-in SDK (auto-builds the tree)

npm i openai            # the wrapper extends the official SDK

const { ForkMindOpenAI } = require('forkmind');

const client = new ForkMindOpenAI({
  apiKey: 'ollama',                       // ignored by Ollama; required by SDK
  upstream: 'http://localhost:11434',     // free local open-source models
});

// Each call is recorded; sequential calls auto-chain into a conversation tree.
const res = await client.chat.completions.create({
  model: 'llama3',
  messages: [{ role: 'user', content: 'Explain backpropagation simply.' }],
});

Run the full example:

node examples/chain.js

Any language — point your client at the proxy

The SDK wrapper is convenience, not a requirement. ForkMind's proxy speaks the OpenAI-compatible wire protocol, so capture works from any language: set your client's base URL to http://localhost:4500/v1 and you're recorded. Chain turns into a tree by passing back the x-forkmind-node-id from the previous response as the next request's x-forkmind-parent header (the JS wrapper just automates this).

# Python — official openai client, zero ForkMind code
from openai import OpenAI

client = OpenAI(base_url="http://localhost:4500/v1", api_key="ollama")
res = client.chat.completions.create(
    model="llama3",
    messages=[{"role": "user", "content": "Explain backpropagation simply."}],
    extra_headers={"x-forkmind-upstream": "http://localhost:11434"},
)
# read res via .with_raw_response to grab x-forkmind-node-id and chain the next call

# curl — anything that can POST JSON
curl http://localhost:4500/v1/chat/completions \
  -H 'content-type: application/json' \
  -H 'x-forkmind-upstream: http://localhost:11434' \
  -d '{"model":"llama3","messages":[{"role":"user","content":"hi"}]}' -i
# response header `x-forkmind-node-id: <id>` → pass as `x-forkmind-parent` next call

Go, Ruby, Rust, Java — same deal: base URL + the two headers. The dashboard, branching, MCP, and regression testing all work regardless of source language.

Framework integrations

ForkMind ships thin adapters for the two biggest JS LLM ecosystems. Both route through the same proxy, so capture, branching, the dashboard, MCP, and regression all work unchanged — no model-class swap, no callbacks.

LangChain.js

npm i @langchain/openai @langchain/core

const { ChatOpenAI } = require('@langchain/openai');
const { forkmind } = require('forkmind/langchain');

const fm = forkmind({ upstream: 'http://localhost:11434' }); // free local Ollama
const model = new ChatOpenAI({
  apiKey: 'ollama',
  model: 'llama3',
  configuration: fm.configuration, // baseURL → proxy + chaining fetch
});

await model.invoke('Explain backpropagation simply.');
// sequential calls on `fm` auto-chain; fm.setParent(id) to branch from a node.

Vercel AI SDK

npm i ai @ai-sdk/openai

const { generateText } = require('ai');
const { forkmindOpenAI } = require('forkmind/vercel');

const openai = forkmindOpenAI({ upstream: 'http://localhost:11434' });
const { text } = await generateText({
  model: openai('llama3'),
  prompt: 'Explain backpropagation simply.',
});
// openai.setParent(id) / openai.resetParent() control the branch point.

Both honor FORKMIND_PROXY (proxy base URL) and take an explicit baseURL / upstream per instance.

Using other free / open providers

ForkMind is provider-agnostic — it forwards your auth headers verbatim and lets you set the upstream per client. Anything OpenAI-compatible just works:

new ForkMindOpenAI({ apiKey: process.env.GROQ_API_KEY,
                     upstream: 'https://api.groq.com/openai' });

You can also override per request with the x-forkmind-upstream header if you call the proxy directly instead of via the SDK.

Anthropic (Claude)

npm i @anthropic-ai/sdk

const { ForkMindAnthropic } = require('forkmind');
const client = new ForkMindAnthropic({ apiKey: process.env.ANTHROPIC_API_KEY });
await client.messages.create({ model: 'claude-3-5-sonnet-latest', max_tokens: 512,
                               messages: [{ role: 'user', content: 'hi' }] });

How it works

your app ──▶ ForkMindOpenAI (baseURL = localhost:4500/v1)
                │  injects x-forkmind-parent
                ▼
         ForkMind proxy (Express, :4500)
                │  forwards verbatim (your key, your upstream)
                ▼
         provider (Ollama / Groq / OpenAI / ...)
                │  response
                ▼
         proxy reconstructs + saveNode()  ──▶  .forkmind/nodes/<id>.json
                │  returns x-forkmind-node-id
                ▼
         wrapper chains it as the next call's parent

• Deterministic node IDs. sha256(request + parentId) → first 12 hex chars. Same prompt under the same parent collapses to one node. The ID doesn't depend on the response, so it can be returned as a header even before a streamed body finishes.

• Streaming. Bytes pass through to your app untouched (real SSE); the proxy tees them, reconstructs the full message (text and fragmented tool-call arguments), and saves the node on stream end.

• Branching. Each node records its provider + upstream, so "Fork from here" in the dashboard replays the edited request to the same host, linked to the historical parent.

MCP — let agents query their own history

ForkMind ships an MCP server so an AI agent can read its own .forkmind/ history mid-task and self-correct — recall what it already tried, see how it reached a state, or search past attempts.

forkmind mcp          # stdio MCP server (or: forkmind-mcp)

One-line install via Smithery (configured in smithery.yaml) — run it from your project root so it sees your .forkmind/:

npx -y @smithery/cli install forkmind --client claude

…or register it manually with any MCP client (Claude Desktop / Claude Code / Cursor / Cline):

{
  "mcpServers": {
    "forkmind": {
      "command": "npx",
      "args": ["-y", "github:medhovarsh/forkmind", "mcp"]
    }
  }
}

Tools exposed:

The server reads the .forkmind/ in its working directory — point the client's cwd at your project.

Context capsules — offload context as an encrypted DAG

Most context managers treat a full window as a cache-eviction problem: truncate and lose it. ForkMind capsules invert that — persist first, verify, then compact. A capsule is an immutable, content-addressed DAG of context segments, AES-256-GCM encrypted on disk, restorable in full or one segment at a time.

# Save (items JSON from a file or stdin), get back a 12-char handle
echo '{"title":"auth debug","items":[{"role":"user","content":"..."}]}' \
  | forkmind context save --digest "oauth loop root-caused; fix in token.js"

forkmind context list                 # all capsules, newest first
forkmind context show 9f3ac21b7e04    # decrypt + verify + print
forkmind context verify 9f3ac21b7e04  # DAG integrity: parents, acyclicity, hashes
forkmind context forget 9f3ac21b7e04 --confirm 9f3ac21b7e04   # crypto-shred

Same engine over HTTP (POST/GET/DELETE :4500/api/context…) and via five MCP tools, so agents can archive their own context mid-task and pull it back later. The Claude Code plugin ships a forkmind-archivist skill + subagent that teaches Claude the offload contract: save → verify on disk → only then drop it from the window.

Guarantees:

• Immutable & acyclic by construction — segment ids are hashes over content + parents (Git-style); a cycle would require a hash to contain itself.

• No plaintext at rest — per-capsule keys, wrapped by a master key stored outside .forkmind/ (~/.forkmind-keys/); an accidentally committed .forkmind/ leaks only ciphertext and structure.

• Digests are opt-in — the agent writes a ≤5-line retrieval summary, or omits it entirely for private capsules.

• Forgetting is real — delete destroys the key first (crypto-shredding), then tombstones the id so identical content can never resurrect it.

• The model is never touched — capsules operate on what the client sends; provider, weights, and KV cache are out of scope by design.

RAID — Redundant Array of Independent DAGs

Mirror capsules to any number of extra filesystem targets (second disk, synced folder, network mount). Replicas hold ciphertext + manifests only — keys are never replicated. If the primary copy is lost or bit-rots, restore self-heals from the first replica that passes verification; healed copies get no trust shortcut (full integrity check still runs).

forkmind context replicas add D:\backup\forkmind   # add target + sync
forkmind context replicas list                     # coverage per target
forkmind context replicas sync                     # catch up offline targets,
                                                   # propagate tombstones

Forgetting reaches every copy: reachable replicas are shredded immediately; a replica that was offline gets its stale ciphertext removed on the next sync (tombstone propagation) — and it was unreadable anyway, since the capsule key died at forget time. Tombstones also make heal refuse to resurrect anything forgotten.

Portable export/import

Move a capsule to another machine or project — a laptop that doesn't share this project's ~/.forkmind-keys/ master key, a teammate, cold storage:

forkmind context export 9f3ac21b7e04 --passphrase "correct horse battery staple" --out capsule.json
# ... move capsule.json anywhere ...
forkmind context import capsule.json --passphrase "correct horse battery staple"

The bundle carries its own scrypt-derived key material (N=32768, deliberately slow to resist offline brute force of a weak passphrase) — it never depends on the source machine's master key, and the passphrase is never written into the bundle itself. On import, every segment is independently re-verified (recomputed id, recomputed hash, resolved parents, acyclic DFS) before anything touches disk — the bundle is never trusted blindly, only proven. Import is idempotent and honors tombstones, same as a fresh save.

Archive straight from the capture DAG

The two halves connect: any conversation the proxy captured can be archived into a capsule in one move — no manual JSON assembly — and restored later as a provider-ready messages[] array, ready to splice into the next request:

# archive the whole lineage ending at a captured turn
forkmind context save --from-node a1b2c3d4e5f6 --digest "auth debug, resolved"

# restore as chat messages (or GET /api/context/:id/messages)
forkmind context show 9f3ac21b7e04 --messages

Same via MCP: forkmind_context_save { fromNodeId } and forkmind_context_restore { asMessages: true } — an agent can archive its own captured history mid-task and splice it back whenever needed. Capsules keep sourceNodeIds links back into the turn DAG.

Token savings

forkmind context save and forkmind context stats report an estimated token count freed from your context window (~4 bytes/token, the standard rough heuristic) — a concrete number for how much a capsule actually saved.

Regression testing — pin good outputs, catch degradation

Tweaking a system prompt or swapping a model can silently degrade results. ForkMind lets you pin a known-good captured node as a baseline, then re-run its exact request later and check the new output for drift.

# 1. Pin a good node (grab its id from the dashboard or forkmind_recent)
forkmind regression pin a1b2c3d4e5f6 \
  --name octopus-fact \
  --contains "hearts" \
  --regex "blue|copper" \
  --min-similarity 0.5

# 2. List / remove cases
forkmind regression list
forkmind regression remove octopus-fact

# 3. Re-run after changing prompts/models (exit code 1 if any case fails — CI-ready)
forkmind regression run                 # keyless local (Ollama)
forkmind regression run --key $GROQ_API_KEY --upstream https://api.groq.com/openai

Each case checks the replayed output against:

• contains — substrings that must appear

• not-contains — substrings that must NOT appear

• regex — patterns that must match

• min-similarity — Jaccard word-overlap vs the baseline (drift guard; defaults to 0.3 so a wildly different answer fails even without explicit assertions). LLM output is non-deterministic, so prefer assertions over exact match.

Cases are JSON in .forkmind/regressions/ — commit them to share baselines and gate prompt changes in CI.

Zero cost & local

• No paid API required — defaults to free local models via Ollama.

• No database — every turn is a plain JSON file under .forkmind/.

• No account, no telemetry — nothing leaves your machine except the LLM call you were already making (relayed verbatim to the provider you choose).

.forkmind/ layout

.forkmind/
├── nodes/
│   ├── a1b2c3d4e5f6.json     # one node per turn
│   └── ...
├── contexts/                 # encrypted context capsules
│   └── 9f3ac21b7e04/
│       ├── manifest.json     # public: DAG shape, hashes, opt-in digest
│       └── seg-<id>.enc      # AES-256-GCM ciphertext per segment
├── tombstones.json           # forgotten capsule ids (never resurrected)
└── manifest.json            # version + root node ids

Node schema:

{
  "id": "a1b2c3d4e5f6",
  "parentId": null,           // null = root
  "timestamp": "2026-01-01T00:00:00.000Z",
  "request":  { /* the exact request body */ },
  "response": { /* full or stream-reconstructed response */ },
  "meta": { "provider": "openai", "upstream": "http://localhost:11434", "stream": true },
  "children": ["..."]         // child node ids
}

CLI

Env vars: FORKMIND_PORT, FORKMIND_HOST (default 127.0.0.1 — loopback only; set 0.0.0.0 to expose on the LAN at your own risk), FORKMIND_OPENAI_UPSTREAM, FORKMIND_ANTHROPIC_UPSTREAM, FORKMIND_PROXY (SDK target base URL), FORKMIND_KEY_DIR (capsule master-key location, default ~/.forkmind-keys).

Development

npm install            # installs proxy + dashboard (npm workspaces)
npm test               # jest: hashing, storage, stream reconstruction, API
npm run dashboard:dev  # vite dev server on :5173, proxies API to :4500
npm run dashboard:build
npm run lint

Releasing to npm

Publishing is tag-driven via .github/workflows/release.yml (needs an NPM_TOKEN repo secret with publish rights):

npm version patch        # bumps package.json + tags
git push --follow-tags   # tag push → CI lints, tests, builds dashboard, publishes

prepack rebuilds dashboard/dist so the tarball always ships the UI.

See CONTRIBUTING.md.

Roadmap

• CLI + deterministic storage engine

• Provider-agnostic proxy (OpenAI-compatible + Anthropic) with streaming

• Drop-in SDK wrappers with auto-chaining

• React Flow dashboard + branch execution

• MCP integration — let agents query their own .forkmind/ history

• Automated regression: pin "good" branches, re-run on prompt edits

• Context capsules — offload context as an encrypted, immutable DAG; restore in full or per segment; crypto-shred to forget

• RAID — Redundant Array of Independent DAGs: replicate capsules across filesystem targets with self-healing restore and tombstone propagation

• Capsule export/import — portable, passphrase-encrypted bundles to move context between machines and projects, independently re-verified on import

• Dashboard capsule panel — browse capsules, inspect the DAG segment map, and run integrity verification from the UI (read-only by design)

License

MIT